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Systems biology seeks to understand the cell by studying interactions between its various components and the properties that emerge from these interaction networks. The integration of diverse network types has several important applications including diagnosing metabolic disorders and discovering novel drug targets. A cardinal challenge in this process is the integration of transcriptional regulatory networks with the corresponding metabolic network. We propose a new method called Probabilistic Regulation of Metabolism (PROM) which achieves this by performing automated integrated modeling of regulatory and metabolic networks using high throughput data. Using PROM, we construct an integrated regulatory-metabolic network for the model organism, Escherichia coli, and demonstrate PROM's ability to model the effect of perturbations to transcriptional regulators and predict microbial growth. After validating the approach, we then applied the method to build the first genome-scale integrated metabolic–regulatory model for Mycobacterium tuberculosis, a critically important human pathogen. This study incorporates data from over 1300 microarrays, 2000 transcription factor–target interactions regulating 3300 metabolic reactions, and 1905 knockout phenotypes for E. coli and M. tuberculosis. PROM has the ability to identify drug targets and was able to predict the growth phenotypes with accuracies as high as 90%. Importantly, this method represents the successful integration of two important classes of systems biology models that are rarely combined quantitatively.